Estimation and distribution design method for residual satellite capacity in same shell layer
By employing a hierarchical spatial grid partitioning and adaptive neighborhood search method, the challenge of capacity assessment for mega-constellations in low and medium orbits was solved, enabling efficient and accurate capacity assessment and distribution design, applicable to complex multi-entity heterogeneous environments.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA ACADEMY OF SPACE TECHNOLOGY
- Filing Date
- 2025-12-09
- Publication Date
- 2026-04-24
AI Technical Summary
Existing technologies are insufficient for efficiently assessing the constellation capacity of giant heterogeneous constellations in low-to-medium orbit environments, and lack direct guidance on the distribution and constraint relationships of individual orbits, thus limiting the reference value of on-orbit deployment patterns.
A method combining hierarchical spatial grid partitioning and adaptive neighborhood search is adopted. Local candidate satellite sets are generated through adaptive neighborhood search and greedy strategy, and global conflict detection and resolution are performed to obtain a conflict-free distribution scheme for the remaining satellites.
It achieves efficient and accurate capacity assessment in complex multi-entity heterogeneous environments, is applicable to any satellite distribution, provides a generalized orbital capacity assessment scheme, and ensures strict adherence to existing satellite constraints and computational efficiency.
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Figure CN121920183A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of giant constellation capacity analysis technology, specifically to a method for estimating and distributing the remaining satellite capacity within the same shell. Background Technology
[0002] Orbital location resources have long been a key focus in the aerospace and communications fields. Traditionally, the emphasis on orbital resources has been concentrated on geostationary orbit, due to its fixed location and scarcity. In contrast, low and medium orbits (LEOs) are considered relatively abundant because satellites can be deployed at different altitudes and orbital planes. However, with the rapid advancement of mega-constellations and multi-entity orbital insertion operations, the spatial density of LEOs has increased significantly, leading to more intersections and approach states between orbital planes, and consequently, an increased risk of satellite collisions and operational interference. Against this backdrop, capacity assessment and high-density constellation optimization design for LEOs have significant engineering and safety value. The constellation capacity problem within the same spherical shell is generally an NP-hard problem, making it difficult to obtain an exact global optimum using polynomial-time algorithms. Heuristic or approximate algorithms are typically required to obtain feasible solutions. Existing research largely focuses on constellation analysis under specific configurations or controlled assumptions, limiting its reference value for the current multi-entity, heterogeneous, and rapidly changing on-orbit deployment landscape. While statistical modeling methods can provide macroscopic assessments, they often lack direct guidance on individual orbital distributions and constraints.
[0003] Therefore, there is an urgent need to propose capacity assessment and distributed design methods that take into account scalability, configuration independence, and constraint adaptability in order to meet the practical engineering needs of giant heterogeneous constellation environments. Summary of the Invention
[0004] In view of the above-mentioned technical problems, this invention proposes a method for estimating and designing the remaining satellite capacity within the same shell. Specifically, it is based on the existing satellite distribution characteristics and integrates two major technologies: hierarchical spatial grid partitioning and adaptive neighborhood search. This method is based on the orbital dynamics model and deeply combines orbital occupancy constraints and collision avoidance constraints. It can calculate and output the estimated value of the remaining satellite capacity that the system can accommodate under given operating conditions. Its core advantage is that it is a generalized orbital capacity assessment scheme that does not depend on specific satellite configurations.
[0005] The technical solution to the technical problem of this invention is: a method for estimating and distributing the remaining satellite capacity within the same shell, comprising the following steps:
[0006] Step S1: Based on the existing satellite distribution information and the preset inter-satellite safety constraints, divide the target orbit shell into multiple spatial regions;
[0007] Step S2: Within each of the spatial regions, based on adaptive neighborhood search and a greedy strategy, generate a local candidate satellite set that satisfies the inter-satellite security constraints and includes all existing satellites within the region.
[0008] Step S3: Perform global conflict detection and resolution on the local candidate satellite sets in all space regions to obtain a globally conflict-free remaining satellite distribution scheme, which serves as the remaining capacity estimation result of the target orbital shell.
[0009] According to a technical solution of the present invention, step S1 specifically includes:
[0010] Step S11: Discretize the space of the target orbital shell into a grid at a preset resolution;
[0011] Step S12: Map candidate points, including existing satellite points, to the cells of the grid;
[0012] Step S13: Merge adjacent grid cells based on a breadth-first strategy to form the spatial region, and prioritize processing cells containing existing satellite points during the merging process.
[0013] According to one technical solution of the present invention, step S2 specifically includes:
[0014] For points within the region, the neighborhood search radius used to determine conflicts is dynamically adjusted based on the local point density.
[0015] Based on the adjusted neighborhood search radius, a greedy strategy is used to select candidate points that satisfy the inter-satellite security constraints with all existing satellites and other selected points in the region, in order to construct the local candidate satellite set.
[0016] According to one technical solution of the present invention, after completing the main loop selection using a greedy strategy, the method further includes:
[0017] A second scan is performed on the remaining candidate points within the region to expand the size of the local candidate satellite set.
[0018] According to a technical solution of the present invention, in step S3, global conflict detection and resolution are performed on the local candidate satellite sets of all space regions, specifically including:
[0019] Merge the local candidate satellite sets of all space regions into a global candidate set;
[0020] Traverse the global candidate set. If a candidate point satisfies the inter-satellite security constraints with the global solution set and all existing satellite points, add it to the final solution set; otherwise, remove it.
[0021] According to one technical solution of the present invention, in the global conflict detection and resolution process, the processing priority is set according to the local density of candidate points.
[0022] According to one technical solution of the present invention, the inter-satellite security constraint is determined based on whether the minimum spatial angular distance between two satellites is greater than or equal to a preset threshold.
[0023] According to one technical solution of the present invention, the minimum spatial angular distance is calculated based on the orbital inclination of the two satellites, the right ascension of the ascending node, and the initial angular distance between the ascending nodes.
[0024] According to a technical solution of the present invention, for objects located in the same circular orbital shell with parameters respectively , The two satellites, then the spatial angular distance between the two satellites satisfy:
[0025]
[0026] in, , ,
[0027] , ,
[0028] ,
[0029] , ,
[0030] Then the minimum spatial angular distance correspond The maximum value is expressed as:
[0031] ,
[0032] ;
[0033] Therefore, inter-satellite safety constraints Represented as:
[0034] =
[0035] .
[0036] According to one aspect of the present invention, a system for estimating and distributing the remaining satellite capacity within the same shell for implementing the method described in any of the above technical solutions is proposed, comprising:
[0037] The region division module is used to divide multiple spatial regions within the target orbital shell based on the existing satellite distribution information and preset inter-satellite safety constraints.
[0038] The local optimization module is used to generate a set of local candidate satellites that satisfy the inter-satellite security constraints and include all existing satellites in each of the spatial regions, based on adaptive neighborhood search and a greedy strategy.
[0039] The global integration module is used to perform global conflict detection and resolution on the local candidate satellite sets in all space regions to obtain a globally conflict-free remaining satellite distribution scheme, which serves as the remaining capacity estimation result of the target orbital shell.
[0040] Compared with the prior art, the present invention has the following advantages:
[0041] The present invention provides a method for estimating and distributing the remaining satellite capacity within the same shell, which does not depend on any specific satellite constellation configuration. Its core algorithm is based on orbital dynamics and spatial geometric constraints, and can handle arbitrarily distributed existing satellite point sets. This makes the present invention applicable to the current complex, multi-entity heterogeneous on-orbit environment, providing a unified solution for capacity assessment in various scenarios, and has strong versatility and adaptability.
[0042] This invention employs a "layered spatial grid construction and regionalization" strategy to discretize and regionalize the hard constraints of continuous space and existing satellites, prioritizing the coverage of the neighborhood of existing satellites. Existing satellite points are forcibly included in local optimization and used as the core of the constraints. A protection mechanism is implemented in global resolution, ensuring that the algorithm strictly adheres to the "immovable" constraints of existing satellites throughout the entire process.
[0043] This invention introduces an "adaptive neighborhood search mechanism," which dynamically adjusts the search range based on the local density of candidate points. A smaller radius is used in densely populated regions to improve accuracy, while a larger radius is used in sparsely populated regions to enhance efficiency. Combining a "greedy strategy" with "remedial scanning," a high-quality near-optimal solution can be obtained within an acceptable computation time, effectively balancing the efficiency and accuracy requirements of solving large-scale NP-hard problems. Attached Figure Description
[0044] Figure 1 This is a flowchart illustrating a method for estimating and distributing the remaining satellite capacity within the same shell, according to one embodiment of the present invention.
[0045] Figure 2 A schematic diagram of satellite distribution, illustrating an application example of an algorithm for estimating and designing the remaining satellite capacity within the same shell layer provided by this invention.
[0046] Figure 3 This invention provides an algorithm for estimating and designing the remaining satellite capacity within the same shell, along with constraints on the design of the remaining satellites. The resulting diagram illustrates the satellite distribution implementation. Detailed Implementation
[0047] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.
[0048] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. The embodiments cannot be described in detail here, but the embodiments of the present invention are not limited to the following embodiments.
[0049] like Figure 1 As shown, the present invention provides a method for estimating and distributing the remaining satellite capacity within the same shell, comprising the following steps:
[0050] Step S1: Spatial region division: Based on the existing satellite distribution information and the preset inter-satellite safety constraints, multiple spatial regions are divided within the target orbital shell.
[0051] The purpose of this step is to decompose the continuous, complexly constrained global spatial problem into a series of discrete, relatively independent sub-region problems, laying the foundation for parallel or serial processing. The input is an existing set of satellite points. Minimum interstellar angular distance threshold Grid resolution Adaptive Factor Constraint function f, upper limit of iteration Furthermore, it is necessary to perform validity checks on the input data, map existing satellite point sets to global indices on candidate point sets, set key parameters, and construct... Indexes are used to speed up neighborhood queries.
[0052] Step S2, Adaptive Optimization within the Region: Within each defined spatial region, based on adaptive neighborhood search and a greedy strategy, a local candidate satellite set is generated that satisfies inter-satellite security constraints and must include all existing satellites within the region.
[0053] Step S3, Global Conflict Resolution: Global conflict detection and resolution are performed on the local candidate satellite sets in all space regions to obtain a globally conflict-free remaining satellite distribution scheme, which serves as the remaining capacity estimation result of the target orbital shell.
[0054] Since the regional divisions are independent, cross-regional conflicts may exist between the local candidate sets generated in different regions (i.e., they may not meet inter-satellite safety constraints). This step performs global conflict detection and resolution on all local candidate satellite sets, removing candidate points that conflict with the global solution set or existing satellites in other regions, and finally integrating them to obtain a globally consistent and conflict-free remaining satellite distribution scheme. The total number of satellite points in this scheme is the estimated remaining capacity of the target orbital shell.
[0055] This invention decomposes the complex global capacity assessment problem into three stages: region partitioning, local optimization, and global integration. This method effectively reduces the risk of dimensionality explosion from directly solving the problem. First, region partitioning respects and highlights the constraints of the existing satellite distribution. Second, local optimization allows for efficient searching based on the characteristics of each region. Finally, global integration ensures the feasibility of the overall solution.
[0056] In some embodiments of the present invention, step S1 specifically includes:
[0057] Step S11, Spatial Discretization: The target orbital shell is spatially discretized into a grid at a preset resolution; each grid cell records all points falling within it (including existing satellites and candidate satellites).
[0058] Step S12: Map candidate points, including existing satellite points, to the cells of the grid;
[0059] Step S13: Based on the breadth-first search (BFS) strategy, merge adjacent grid cells to form the spatial region, and prioritize processing cells containing existing satellite points during the merging process.
[0060] Based on a breadth-first search (BFS) strategy, all grid cells are traversed. Starting with an unvisited cell, it is marked as the seed for the current region. Then, cells adjacent to it (adjacent in all three dimensions) are explored. If an adjacent cell meets the merging criteria (e.g., not empty or containing candidate points), it is merged into the current region, and expansion continues outward. This process is repeated until no new adjacent cells can be merged, thus forming a spatially connected region. Then, the next unvisited cell is selected as the new seed, and the construction of the next region begins, until all cells have been visited.
[0061] During the BFS merging process, grid cells containing existing satellite points are prioritized. This means that during region expansion, the algorithm preferentially grows towards adjacent cells containing existing satellite points. This strategy ensures that each resulting spatial region fully includes all existing satellite points within it, and the local neighborhoods of these existing satellite points are completely included in the region for subsequent optimization. This avoids the drawback of existing satellite points being placed at the edge of the region and not being fully considered due to improper region segmentation.
[0062] In some embodiments of the present invention, step S2 specifically includes:
[0063] For points within the region, the neighborhood search radius used to determine conflicts is dynamically adjusted based on the local point density.
[0064] Based on the adjusted neighborhood search radius, a greedy strategy is used to select candidate points that satisfy the inter-satellite security constraints with all existing satellites and other selected points in the region, so as to construct the local candidate satellite set.
[0065] Suppose we are currently processing a certain spatial region .
[0066] 1. Adaptive neighborhood radius calculation: For each candidate point within the region... The algorithm does not use a fixed global distance threshold as the neighborhood range, but rather uses candidate points... The density of surrounding local points is dynamically adjusted. The specific steps are as follows:
[0067] a. Calculate candidate points Local density Candidate points can be calculated. The density is determined by the average distance to its k nearest neighbors; a smaller average distance results in a higher density.
[0068] b. Local average neighborhood radius From the formula The calculation yielded the following. It is the angular distance threshold for inter-satellite safety constraints. It is an adjustable adaptive factor (e.g., 0.5~2.0).
[0069] In regions with dense candidate points, local density Smaller, neighborhood radius The density is also relatively small, allowing for more refined and rigorous conflict checks to avoid mistakenly selecting points that are too close together. In regions where candidate points are sparse, density(q) is larger, and r(q) is correspondingly increased, enabling the discovery of potential conflict points at greater distances and ensuring the completeness of the search. The adaptive mechanism intelligently balances the needs for search accuracy and efficiency in different regions.
[0070] 2. Set construction based on a greedy strategy:
[0071] a. Initialization: Set the region All existing satellite points within the local solution set must be forcibly added. This constraint is a hard constraint that cannot be violated.
[0072] b. Calculate neighborhood degree: for each candidate point within the region In its adaptive neighborhood radius Within, count how many points are candidate points. Meet inter-satellite safety constraints At the same time, it is also necessary to ensure the candidate points With local solution set All existing satellite points in the middle also meet the constraints. Points that satisfy both conditions are denoted as candidate points. The "neighborhood degree".
[0073] c. Sorting and Selection: Sort all candidate points in descending order of their "neighborhood degree". Prioritize those points that are compatible with more points in their neighborhood (i.e., have fewer conflicts), as these are more likely to be added to the solution set and leave more room for the addition of subsequent points.
[0074] d. Iterative addition: Starting from the top of the sorted list, check each candidate point in turn. If candidate points With the current solution set All points (including existing satellites and added candidate points) satisfy the constraints. Then the candidate points Add to the current solution set And remove all candidate points from the candidate list. Points of conflict. Repeat this process until the list is empty or the iteration limit is reached.
[0075] This implementation combines an adaptive mechanism with a greedy strategy, forming the core of local optimization. The adaptive radius solves the search problem caused by non-uniform distribution, while the greedy strategy (sorted by neighborhood degree) guides the search towards the most lenient solution space, accommodating the most points. Enforcing the inclusion of existing satellite points as the core constraint ensures absolute obedience of local solutions to hard constraints. This method can quickly generate a large-scale, high-quality set of compatible points for each region, laying a solid foundation for global integration.
[0076] In some embodiments of the present invention, after the main loop selection is completed using a greedy strategy, the method further includes:
[0077] A second scan is performed on the remaining candidate points within the region to expand the size of the local candidate satellite set.
[0078] After completing the main loop of the greedy strategy, some candidate points may still remain unselected or not explicitly marked as conflicting with the solution set. To maximize the potential of the region and expand the size of the local candidate set, the algorithm performs a remedial scan.
[0079] Iterate through the remaining candidate points that were skipped or removed in the main loop. For each such point... Check again whether it matches the current local solution set. All points satisfy inter-satellite safety constraints If satisfied, add it. .because After the main loop has stabilized, this scan is a low-cost leak detection operation, but it can often add some compatible points, thereby improving the saturation of local solutions and the accuracy of the final capacity estimate.
[0080] Remedial scanning is a simple and effective post-optimization step that can overcome the omission problem that greedy algorithms may cause due to the early selection order. Without significantly increasing the computational complexity, it performs a "gap-filling" of the local solution, which helps to approximate the maximum number of points that the region can theoretically accommodate, making the local optimization results more reliable and complete.
[0081] In some embodiments of the present invention, step S3 involves performing global conflict detection and resolution on the local candidate satellite sets for all space regions, specifically including:
[0082] Merge the local candidate satellite sets of all space regions into a global candidate set;
[0083] Traverse the global candidate set. If a candidate point satisfies the inter-satellite security constraints with the global solution set and all existing satellite points, add it to the final solution set; otherwise, remove it.
[0084] In some embodiments of the present invention, during the global conflict detection and resolution process, the processing priority is set according to the local density of candidate points.
[0085] In some embodiments of the present invention, the inter-satellite security constraint is determined based on whether the minimum spatial angular distance between two satellites is greater than or equal to a preset threshold.
[0086] In some embodiments of the present invention, the minimum spatial angular distance is calculated based on the orbital inclination of the two satellites, the right ascension of the ascending node, and the initial angular distance between the ascending nodes.
[0087] Considering the orbital altitude is The orbital inclination angle is The right ascension of the ascending node is A circular orbiting satellite. Let its initial ascending node angular distance be... Then the position vector of the satellite in the geocentric inertial coordinate system can be expressed as:
[0088] (1)
[0089] (2)
[0090] in This represents the satellite's angular velocity. For satellites within the same spherical shell, the angular velocity remains constant.
[0091] Let the parameters of any two satellites within the same spherical shell be respectively , The spatial angular distance between the two satellites is denoted as .
[0092] The cosine value of this spatial angular distance can be represented by the dot product of the position vectors in the ECI coordinate system:
[0093] (3)
[0094] (4)
[0095] in, , ,
[0096] , ,
[0097] ,
[0098] , .
[0099] Further analysis reveals the minimum spatial angular distance. correspond The maximum value of is expressed by the following formula:
[0100] (5)
[0101] (6)
[0102] Therefore, the constraints are set as follows:
[0103] =
[0104] (7)
[0105] The following application example demonstrates the specific application process and effects of the method of this invention. Capacity estimation is performed using a shell layer from the first phase of the Starlink constellation as an example.
[0106] by 53° tilt angle Taking a constellation as an example, assuming its orbital altitude is 550km and it contains a total of 1080 satellites, including 72 orbital planes with 15 satellites per plane (53°: 1080 / 72 / 7), the minimum inter-satellite distance can be calculated to be 94km.
[0107] If the original minimum inter-satellite distance constraint is maintained and the existing satellite parameters are substituted into the proposed algorithm, it can be estimated that, without considering configuration constraints and only estimating from the perspective of the same spherical shell, the upper limit of the remaining satellite capacity within a 550km spherical shell under the same minimum inter-satellite angular distance constraint is approximately 1748 satellites, distributed as follows: Figure 2 As shown. If the tilt angle is maintained at 53°, the maximum remaining satellite capacity is approximately 1093 satellites, distributed as follows. Figure 3 As shown.
[0108] According to one aspect of the present invention, a system for estimating and distributing the remaining satellite capacity within the same shell for implementing the method described in any of the above technical solutions is proposed, comprising:
[0109] The region division module is used to divide multiple spatial regions within the target orbital shell based on the existing satellite distribution information and preset inter-satellite safety constraints.
[0110] The local optimization module is used to generate a set of local candidate satellites that satisfy the inter-satellite security constraints and include all existing satellites in each of the spatial regions, based on adaptive neighborhood search and a greedy strategy.
[0111] The global integration module is used to perform global conflict detection and resolution on the local candidate satellite sets in all space regions to obtain a globally conflict-free remaining satellite distribution scheme, which serves as the remaining capacity estimation result of the target orbital shell.
[0112] According to one aspect of the present invention, an electronic device is provided, comprising: one or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, and the one or more computer programs are stored in the memory; when the electronic device is running, the processor executes the one or more computer programs stored in the memory to cause the electronic device to perform a method for estimating and distributing the remaining satellite capacity within the same shell as described in any of the above technical solutions.
[0113] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.
[0114] The memory can be an internal storage unit of the terminal device, such as a hard drive or RAM. Alternatively, it can be an external storage device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory can include both internal and external storage units. The memory is used to store the computer program and other programs and data required by the terminal device. It can also be used to temporarily store data that has been output or will be output.
[0115] According to one aspect of the present invention, a computer-readable storage medium is provided for storing computer instructions, which, when executed by a processor, implement a method for estimating and distributing the remaining satellite capacity within the same shell, as described in any of the above technical solutions.
[0116] For example, computer-readable storage media can be read-only memory (ROM), random access memory (RAM), read-only optical disc (CD-ROM), magnetic tape, floppy disk, and optical data storage devices. They can be implemented using computer-executable program code, thus allowing them to be stored in a storage device for execution by a computing device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Therefore, this invention is not limited to any particular hardware and software combination.
[0117] In summary, the present invention provides a method for estimating and designing the remaining satellite capacity within the same satellite shell. This method can model the space resources within the target shell based on the existing satellite distribution and estimate the maximum number of satellites that can be safely added. Furthermore, it can be flexibly adjusted according to mission scenarios, providing quantifiable decision support for satellite deployment planning, on-orbit resource management, and capacity prediction.
[0118] This invention, based on an orbital dynamics model and combined with orbital occupancy and collision constraints, calculates and provides an estimate of the remaining satellite capacity that the system can accommodate under given operating conditions. It is an evaluation method that does not depend on a specific satellite configuration.
[0119] This invention introduces an adaptive neighborhood search mechanism, which dynamically adjusts the neighborhood size based on local features of the point cloud, effectively balancing the search accuracy and computational cost in different regions.
[0120] This invention can flexibly adapt to various geometric or distance constraints. It can be applied to specific applications simply by setting the corresponding criteria in neighborhood search and conflict resolution.
[0121] Furthermore, it should be noted that the embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code.
[0122] Embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0123] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing terminal equipment to cause a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0124] It should also be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.
[0125] Finally, it should be noted that the above description represents a preferred embodiment of the present invention. It should be pointed out that although preferred embodiments have been described, those skilled in the art, once they understand the basic inventive concept of the present invention, can make various improvements and modifications without departing from the principles described herein. These improvements and modifications should also be considered within the scope of protection of the present invention. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the present invention.
Claims
1. A method for estimating and distributing the remaining satellite capacity within the same shell, characterized in that, Includes the following steps: Step S1: Based on the existing satellite distribution information and the preset inter-satellite safety constraints, divide the target orbit shell into multiple spatial regions; Step S2: Within each of the spatial regions, based on adaptive neighborhood search and a greedy strategy, generate a local candidate satellite set that satisfies the inter-satellite security constraints and includes all existing satellites within the region. Step S3: Perform global conflict detection and resolution on the local candidate satellite sets in all space regions to obtain a globally conflict-free remaining satellite distribution scheme, which serves as the remaining capacity estimation result of the target orbital shell.
2. The method according to claim 1, characterized in that, Step S1 specifically includes: Step S11: Discretize the space of the target orbital shell into a grid at a preset resolution; Step S12: Map candidate points, including existing satellite points, into the cells of the grid; Step S13: Merge adjacent grid cells based on a breadth-first strategy to form the spatial region, and prioritize processing cells containing existing satellite points during the merging process.
3. The method according to claim 1, characterized in that, Step S2 specifically includes: For points within the region, the neighborhood search radius used to determine conflicts is dynamically adjusted based on the local point density. Based on the adjusted neighborhood search radius, a greedy strategy is used to select candidate points that satisfy the inter-satellite security constraints with all existing satellites and other selected points in the region, in order to construct the local candidate satellite set.
4. The method according to claim 3, characterized in that, After completing the main loop selection using a greedy strategy, the following steps are also included: A second scan is performed on the remaining candidate points within the region to expand the size of the local candidate satellite set.
5. The method according to claim 1, characterized in that, In step S3, global conflict detection and resolution are performed on the local candidate satellite sets for all space regions, specifically including: Merge the local candidate satellite sets of all space regions into a global candidate set; Traverse the global candidate set. If a candidate point satisfies the inter-satellite security constraints with the global solution set and all existing satellite points, add it to the final solution set; otherwise, remove it.
6. The method according to claim 5, characterized in that, In the global conflict detection and resolution process, the processing priority is set according to the local density of candidate points.
7. The method according to any one of claims 1 to 6, characterized in that, The inter-satellite security constraints are determined based on whether the minimum spatial angular distance between two satellites is greater than or equal to a preset threshold.
8. The method according to claim 7, characterized in that, The minimum spatial angular distance is calculated based on the orbital inclination of the two satellites, the right ascension of the ascending node, and the initial angular distance between the ascending nodes.
9. The method according to claim 8, characterized in that, For those located in the same circular orbital shell, with parameters respectively , The two satellites, then the spatial angular distance between the two satellites satisfy: in, , , ， ， ， ， ， Then the minimum spatial angular distance correspond The maximum value is expressed as: ， ； Therefore, inter-satellite safety constraints Represented as: = 。 10. A system for estimating and distributing the remaining satellite capacity within the same shell for implementing the method as described in any one of claims 1 to 9, characterized in that, include: The region division module is used to divide multiple spatial regions within the target orbital shell based on the existing satellite distribution information and preset inter-satellite safety constraints. The local optimization module is used to generate a set of local candidate satellites that satisfy the inter-satellite security constraints and include all existing satellites in each of the spatial regions, based on adaptive neighborhood search and a greedy strategy. The global integration module is used to perform global conflict detection and resolution on the local candidate satellite sets in all space regions to obtain a globally conflict-free remaining satellite distribution scheme, which serves as the remaining capacity estimation result of the target orbital shell.